code: def setup_dummy_dataset(root_dir: Path):
      # Creates a dummy dataset mirroring the structure of Vimeo-90k,
      # where each image in the dataset is filled with a unique constant value.
  
      with open(root_dir / "sep_trainlist.txt", "w") as file:
          file.write("001\n003\n")
  
      with open(root_dir / "sep_testlist.txt", "w") as file:
          file.write("002\n")
  
      for i in range(1, 4):
          folder = root_dir / f"sequences/00{i}"
          folder.mkdir(parents=True)
          for j in range(1, 8):
              img = Image.fromarray(
                  np.ones((10, 10, 3), dtype=np.int8) * (10 * i) + j,
                  "RGB",
              )
              img.save(folder / f"im{j}.png")
  
file_path: NeuralCompression/tests/data/test_vimeo_90k_septuplet.py
imports: from pathlib import Path
  import numpy
  import pytest
  import torch
  from PIL import Image
  from torchvision.transforms import Compose, ToTensor
  from neuralcompression.data._vimeo_90k_septuplet import Vimeo90kSeptuplet
label_desc: Use image node_id 1 for calling a global function with example usage: setup_dummy_dataset(root_dir) without return types